RT Journal Article
T1 Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.
A1 Esteban-Medina, Marina
A1 Peña-Chilet, María
A1 Loucera, Carlos
A1 Dopazo, Joaquín
K1 Big data
K1 Fanconi anemia
K1 Genomics
K1 Machine learning
K1 Mathematical models
K1 Signaling pathways
AB In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases. The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets. The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.
YR 2019
FD 2019-07-02
LK http://hdl.handle.net/10668/14209
UL http://hdl.handle.net/10668/14209
LA en
DS RISalud
RD Apr 6, 2025